Enhanced cell tracking using a GAN-based super-resolution video-to-video time-lapse microscopy generative model.

ABSTRACT: Cells are among the most dynamic entities, constantly undergoing processes like growth, division, movement, and interaction with their environment and other cells. Time-lapse microscopy is central to capturing these dynamic behaviors, providing detailed spatiotemporal information at single-cell resolution in real time. Although deep learning has transformed cell segmentation, cell tracking remains challenging due to limited annotated time-lapse data. To address this, we introduce tGAN, a generative adversarial network (GAN)-based time-lapse microscopy generator that enhances the quality and diversity of synthetic annotated time-lapse microscopy data. Featuring a dual-resolution architecture, tGAN accurately captures both low- and high-resolution cellular details essential for accurate trac

SUBMITTER: Zargari A 

PROVIDER: S-EPMC11994914 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature